TY - GEN
T1 - Fraud detection in electric energy using differential evolution
AU - Brun, Angelo Darcy Molin
AU - Pinto, João Onofre Pereira
AU - Pinto, Alexandra Maria Almeira Carvalho
AU - Sauer, Leandro
AU - Colman, Evando
PY - 2009/12/9
Y1 - 2009/12/9
N2 - This work proposes the use of deferential evolution algorithm to find the parameters of a data mining system used to pre-select electrical energy consumers with suspect of fraud. A pattern recognition system was built in order to identify suspicious behavior of electrical energy consumers. However, the system only indicates such clients, and the frauds must be confirmed through in locus inspection. For that reason, it is important that true alarms be high to justify the trade-off of the inlocus inspection. Therefore, the parameter of the pattern recognition system must be well tuned, and that can be modeled as an optimization problem using the available training data. This work describes the pattern recognition system in details, and shows the algorithm modeling as an optimization problem. The defferential algorithm will be described and results will be show. Results confirm that this approach is feasible.
AB - This work proposes the use of deferential evolution algorithm to find the parameters of a data mining system used to pre-select electrical energy consumers with suspect of fraud. A pattern recognition system was built in order to identify suspicious behavior of electrical energy consumers. However, the system only indicates such clients, and the frauds must be confirmed through in locus inspection. For that reason, it is important that true alarms be high to justify the trade-off of the inlocus inspection. Therefore, the parameter of the pattern recognition system must be well tuned, and that can be modeled as an optimization problem using the available training data. This work describes the pattern recognition system in details, and shows the algorithm modeling as an optimization problem. The defferential algorithm will be described and results will be show. Results confirm that this approach is feasible.
KW - Differential evolution
KW - Electrical energy consumers
KW - Fraud detection
UR - http://www.scopus.com/inward/record.url?scp=76549118900&partnerID=8YFLogxK
U2 - 10.1109/ISAP.2009.5352917
DO - 10.1109/ISAP.2009.5352917
M3 - Conference contribution
AN - SCOPUS:76549118900
SN - 9781424450985
T3 - 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09
BT - 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09
T2 - 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09
Y2 - 8 November 2009 through 12 November 2009
ER -